Detecting and removing outlier(s) in electromyographic gait-related patterns
In this paper, we propose a method for outlier detection and removal in electromyographic gait-related patterns (EMG-GRPs). The goal was to detect and remove EMG-GRPs that reduce the quality of gait data while preserving natural biological variations in EMG-GRPs. The proposed procedure
consists of general statistical tests and is simple to use. The Friedman test with multiple comparisons was used to find particular EMG-GRPs that are extremely different from others. Next, outlying observations were calculated for each suspected stride waveform by applying the generalized
extreme studentized deviate test. To complete the analysis, we applied different outlier criteria. The results suggest that an EMG-GRP is an outlier if it differs from at least 50% of the other stride waveforms and contains at least 20% of the outlying observations. The EMG signal remains
a realistic representation of muscle activity and demonstrates step-by-step variability once the outliers, as defined here, are removed.
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detecting and removing outliers;
electromyographic gait-related patterns
Document Type: Research Article
School of Electrical Engineering, University of Belgrade, Bulevar kralja Aleksandra 73 11120, Belgrade, Serbia
Faculty of Engineering, University of Novi Sad, Trg Dositeja Obradovica 6 21000, Novi Sad, Serbia
Department of Child Neurology and Electromyoneurography, Clinic for Child Rehabilitation, Institute for Child and Youth Health Care of Vojvodina, Hajduk Veljkova 10 21000, Novi Sad, Serbia
June 1, 2013